mlsanityData to 7 Oct 2026

Local and Global Point Cloud Reconstruction for 3D Hand Pose Estimation

arXiv 2112.06389 ↗First posted 13 Dec 2021cs.CV cs.AIcited by 6

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Abstract (arXiv)

This paper addresses the 3D point cloud reconstruction and 3D pose estimation of the human hand from a single RGB image. To that end, we present a novel pipeline for local and global point cloud reconstruction using a 3D hand template while learning a latent representation for pose estimation. To demonstrate our method, we introduce a new multi-view hand posture dataset to obtain complete 3D point clouds of the hand in the real world. Experiments on our newly proposed dataset and four public benchmarks demonstrate the model's strengths. Our method outperforms competitors in 3D pose estimation while reconstructing realistic-looking complete 3D hand point clouds.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 32 of its 49 references that are in the map. First posted on arXiv 13 Dec 2021.

Builds on

32 of 49 references.

References · 32

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 972
  3. 2016Stacked Hourglass Networks for Human Pose Estimationcited by 913
  4. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
  5. 2018AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generationcited by 539
  6. 2019FreiHAND: A Dataset for Markerless Capture of Hand Pose and Shape from Single RGB Imagescited by 271
  7. 2017Integral Human Pose Regressioncited by 255
  8. 2020Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Posecited by 248
  9. 2017Learning to Estimate 3D Hand Pose from Single RGB Imagescited by 234
  10. 20193D Hand Shape and Pose from Images in the Wildcited by 190
  11. 20193D Hand Shape and Pose Estimation from a Single RGB Imagecited by 174
  12. 2019Tex2Shape: Detailed Full Human Body Geometry From a Single Imagecited by 166
20 more
  1. 2020Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wildcited by 146
  2. 2017Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Modelscited by 142
  3. 2019End-to-end Hand Mesh Recovery from a Monocular RGB Imagecited by 124
  4. 2018Hand Pose Estimation via Latent 2.5D Heatmap Regressioncited by 111
  5. 2017V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Mapcited by 102
  6. 2018Multiresolution Tree Networks for 3D Point Cloud Processingcited by 91
  7. 2018Cross-modal Deep Variational Hand Pose Estimationcited by 89
  8. 2019SampleNet: Differentiable Point Cloud Samplingcited by 74
  9. 2021Real-time Pose and Shape Reconstruction of Two Interacting Hands With a Single Depth Cameracited by 70
  10. 2021Model-based 3D Hand Reconstruction via Self-Supervised Learningcited by 65
  11. 2019A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation from a Single Depth Imagecited by 56
  12. 2017BigHand2.2M Benchmark: Hand Pose Dataset and State of the Art Analysiscited by 46
  13. 2018Disentangling Latent Hands for Image Synthesis and Pose Estimationcited by 44
  14. 20163D Hand Pose Tracking and Estimation Using Stereo Matchingcited by 35
  15. 2016Robust 3D Hand Pose Estimation in Single Depth Images: from Single-View CNN to Multi-View CNNscited by 29
  16. 2020HandVoxNet: Deep Voxel-Based Network for 3D Hand Shape and Pose Estimation from a Single Depth Mapcited by 23
  17. 2018Point-to-Pose Voting based Hand Pose Estimation using Residual Permutation Equivariant Layercited by 21
  18. 2019DenseBody: Directly Regressing Dense 3D Human Pose and Shape From a Single Color Imagecited by 20
  19. 20183D-PSRNet: Part Segmented 3D Point Cloud Reconstruction From a Single Imagecited by 12
  20. 2019Dual Grid Net: hand mesh vertex regression from single depth mapscited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 0; 2026: 2.

6 papers.